English(EN) Active Learning with Bayesian Multi-Fidelity Laplace Neural Operators for Oscillatory Parametric PDEs

新的贝叶斯MF-LNO增强了复杂PDE的主动学习

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一种贝叶斯多保真度拉普拉斯神经网络算子（MF-LNO），用于振荡参数化偏微分方程（PDE）的主动学习。该方法利用副本交换随机梯度Langevin动力学（reSGLD）量化的预测不确定性来指导高保真度训练数据的获取。在Lorenz系统和Duffing振子等系统上的实验表明，这种不确定性指导的方法比随机采样更具数据效率和准确性，并且在预测不确定性量化方面优于MF-DeepONet。 AI

影响该方法为模拟复杂的工程系统提供了一种更具数据效率的方法，有可能加速设计优化和数字孪生应用。

排序理由该集群包含一篇详细介绍求解PDE新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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本文如何被排名

Signal score: 11 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇详细介绍求解PDE新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Bongseok Kim, Haoyang Zheng, Michael Penwarden, Guang Lin · 2026-09-15 04:00

用于振荡参数化偏微分方程的贝叶斯多保真度拉普拉斯神经网络算子的主动学习

arXiv:2502.00550v2 Announce Type: replace Abstract: Surrogate models of parametric dynamical systems are essential for many-query and real-time predictions in engineering applications such as design optimization and digital twins. However, generating high-fidelity (HF) training d…

本文如何被排名

报道来源 [1]

用于振荡参数化偏微分方程的贝叶斯多保真度拉普拉斯神经网络算子的主动学习

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